Decreasing Social Isolation in Adults via a Cognitive Wellness Program
Civic and Community Engagement
Community Health and Preventive Medicine
Mental and Social Health
Translational Medical Research
MetadataShow full item record
AbstractIn October 2015, Buzzards Bay Speech Therapy and Coastline Elderly Services, Inc, collaborated to address concerns regarding healthy aging in New Bedford. According to the 2014 Massachusetts Healthy Aging Data Report, New Bedford scored lowest in the state with regard to healthy aging, with 31 health indicators worse than the state average, including depression, mental illness, stroke and Alzheimer's disease. Recognizing that these indicators can lead to social isolation and further exacerbate health concerns, we developed a program focusing on cognitive wellness in order to enhance social engagement. The goal of the program is to provide evidenced based interventions to adults in order to improve social connectedness, sense of well-being, and communicative effectiveness in order to decrease social isolation, depressive symptoms and caregiver burden. The program uses class-based instruction and lively activities to educate and engage participants while practicing tips and techniques to improve thinking, memory, communication and socialization skills. Quantitative and qualitative outcome data collected from 2015-present reveals that classes are effective at decreasing social isolation, encouraging the formation/renewal of friendships and the trying of new things, and improving confidence in communication skills. Additionally, data reflects that the factor most susceptible to change following participation in our classes is a feeling of optimism, born out of camaraderie within the class, gains in self-confidence and self-acceptance, and motivation to improve. Currently we are initiating Participatory Action to enhance community engagement, expand programming, and identify resources that may be available/created in order to improve cognitive wellness and decrease social isolation.
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/26737
RightsCopyright the Author(s)
Showing items related by title, author, creator and subject.
Physiological and Social Stress on Cognitive PerformanceNagatti, Doreet; Anina, Daniele; Daigle, Maria; O'Brien, Kymberlee M. (2016-05-20)Humans are highly social creatures and this provides us with a number of benefits, such as protection and support, but it also brings new avenues for stress from social sources. Basic and translational neuroendocrine research has yielded a rich set of findings and a general understanding of how acute and chronic stress can result in reduced health, earlier aging, and earlier death. Although stress can be indexed by level of cortisol, the major stress hormone in humans, many interrelated physiological systems are involved in a stress response, including the cardio and vascular systems. Research toward greater understanding of stress buffering mechanisms holds value for improved human health in the face of entrenched social stressors. In particular, acute and chronic stress have consistently been found to impair cognitive performance, Many adults in high stress environments also face a changing social landscape during college years: changes in living partners, less control over noise, sleep, exercise, and nutrition. In this pilot investigation, we are interested in measuring the influences of acute stress on cognitive performance and whether social support, a factor that is modifiable, would be protective on the multi-systems relationships between stress and cognition. Broadly, we found (1) that higher levels of cortisol measured in saliva was associated with a faster return to resting levels of salivary cortisol (a measure of flexible, adaptive functioning of the central HPA stress system) after the stressor is removed and may also be associated with lower cortisol in the initial response to the stressor. In parallel, we found (2) that higher levels of cortisol were associated with impaired cognitive performance after the stress task, (3) finally, we found that those reporting high social support showed faster recovery to baseline in the cardiovascular systems and greater social support produced some buffering of stress response on their post-stress cognitive performance.
Health Applications of Social Network Analysis and Computational Social ScienceKitts, James (2016-05-20)Social network analysis has proliferated across the social and behavioral sciences, shifting our analytical focus from individuals to the patterns of social ties that connect them. This perspective has enriched our understanding of a great variety of health-related phenomena, including the spread of STDs on contact networks, the spread of health care practices on physicians’ professional networks, the dynamics of patient transfers on networks of clinics, and the spread of weight-related behaviors among adolescents at risk for obesity. The advent of the era of computational social science has augmented the contributions of this perspective, by moving beyond expensive and laborious methods of questionnaires and direct observation to incorporate new techniques of data collection and analysis. For example, these include analysis of electronic health records or other time-stamped communication traces among healthcare practitioners; streams of behavioral data from wearable sensors, location-aware devices, or electronic calendars; automated analysis of text in documents; and mapping networks of interaction by citations and collaboration in clinical research literatures. Whereas much of computational social science has offered new ways of monitoring health behavior and healthcare behavior, or for analyzing those data, a further contribution has been to directly analyze these social processes in system dynamics models, microsimulation, and agent-based models. These approaches allow for computational experiments that assist in predicting and interpreting outcomes from health interventions. This poster will highlight some of my recent and pending work in this domain, aiming to identify potential collaborators in UMCCTS for projects that involve social networks or computational social science.